Data-Driven PSP Linkages for Atomistic Datasets

By Gomberg, Joshua A.

Georgia Institute of Technology

Download (PDF)

Licensed according to this deed.

Published on


Advisors: Surya R. Kalidindi, David L. McDowell, Mo Li, Benjamin Haaland, Hamid Garmestani

For a variety of materials, atomic-scale modeling techniques are commonly employed as a means of investigating fundamental properties, including both structural and chemical responses. While force-field based calculations are significantly less computationally expensive than their quantum-mechanical counterparts, the datasets often investigated are large in size (10^3 - 10^9 atoms) and high-dimensional, and thus cumbersome for use in multi-scale models. The development of quantitative "process-structure-property" (PSP) linkages for atomistic simulations presents a powerful route to convert atomistic simulation data into actionable knowledge. Here, a framework is presented for quantifying structure from these simulations in full- and reduced-dimensional form, and a series of protocols are developed for establishing regression models for process-structure and structure-property linkages.

Cite this work

Researchers should cite this work as follows:

  • Gomberg, Joshua A. (2018), "Data-Driven PSP Linkages for Atomistic Datasets,"

    BibTex | EndNote


MATIN Development Team